There might be physiological changes, such as weight loss
or gain, or there might be differences in the ambient temperature, humidity, clothing, and a number of other factors.

Table 1 summarizes results for the two classifiers. Both
classifiers can be tuned by selecting a specific false positive
rate. For example, in a continuous authentication application,
where false negatives are of greater concern, classifiers can
be tuned to a lower false negative rate, by accepting a higher
false positive rate.

8. RELATED WORK

The full version of this paper has a detailed survey of related
work. 8 In this version, we provide a brief overview.

Biometrics, as a means of recognizing an individual using
physiological or behavioral traits, has been an active research
area for many years. A comprehensive survey of conventional
physiological biometrics can be found in Jain et al. 5 While
physiological biometrics tend to be relatively stable over time,
they are sensitive to deception attacks, for example, mock
fingers. 1 In contrast, behavioral biometrics are much harder
to circumvent. However, the performance of behavioral biometric systems is usually worse and can require re-calibration
due to normal variations in human behavior. Initial results
on behavioral biometrics were focused on typing and mouse
movements, for example, Spillane. 9 Keystroke dynamics
became quite popular, 6 as a means to augment password
authentication in manner similar to our PIN-entry scenario.

The result most closely related to our work is Cornelius
et al., 2 where bioimpedance is used as a biometric: a wearable
wrist sensor passively recognizes its wearers based on the
body’s unique response to the alternating current of different
frequencies. Experiments in Cornelius et al. 2 were conducted
in a family-sized setting and show a recognition rate of 90%
when measurements are augmented with hand geometry.
The pulse-response biometric proposed in this paper solves
a different problem but it also uses the body’s response to a
signal. It achieves a recognition rate of 100% when samples
are taken in one session and 88% when samples are taken
weeks apart (no augmentation is required in both cases).

9. CONCLUSION

We proposed a new biometric based on the human body’s
response to an electric square pulse signal. This biometric

All performance figures have been assessed on the basis of test data not involved in
any development or training phase of the classifiers. Values for true/false positives/
negatives are at the equal error rate of EER = 0.00 for the single data set and EER = 1. 12
over time.

can serve an additional authentication mechanism in a PIN
entry system, enhancing security of PIN entry with minimal
extra user burden. The same biometric is applicable to continuous authentication. To this end, we designed a continuous authentication mechanism on a secure terminal, which
ensures user continuity, that is, the user who started the session is the same one who is physically at the terminal keyboard throughout the session.

Through experiments with a proof-of-concept prototype
we demonstrated that each human body exhibits a unique
response to a signal pulse applied at the palm of one hand,
and measured at the palm of the other. Using the prototype
we could identify users—with high probability—in a matter
of seconds. This identification mechanism integrates well
with other established methods, for example, PIN entry, to
produce a reliable added security layer, either on a continuous basis or at login time.